Critical Approaches to #DataScience & #MachineLearning

Posted on March 18th, 2017

Geetu Ambwani @HuffingtonPost @geetuji spoke about how the Huffington Post is looking at data as a way around the filter bubble in which separates individuals from views that are contrary to their previously help beliefs. Filter bubbles are believed to be a major reason for the current levels of polarization in society.

The talked about ways that the media can respond to this confirmation bias

Show opposing point of view

Show people their bias

Show source crediability

For instance, Chrome and Buzzfeed have tools that will insert opposing points of view in your news feed. Flipfeed enables you to easily load another feed. AlephPost clusters articles and color codes them indicating the source’s vantage view. However, showing people opposing views can backfire.

Second, Readacross the spectrum will show you your biases. Politico will show you how blue or red you by indicating the color of your information sources.

Third, one can show source credibility and where it lies on the political spectrum

However, there is still a large gap between what is produced by the media and what consumers want. Also this does not remove the problem that ad dollars are given for “engagement” which means that portals are incented to continue delivering what the reader wants.

Next, Justin Hendrix @NYC Media Lab (consortium of universities started by the city of NY) talked about emerging media technologies. Examples were

Data selfi project – from the new school. See the data which Facebook has on us. A chrome extension. 100k downloads in the first week.

Braiq – connect the mind with the on-board self-driving software on cars. Build software which is more reactive to the needs and wants of the passenger. Technology in the headrest and other inputs that will talk to the self-driving AI.

The follow up discussion covered a wide range of topics including

The adtech fraud is known, but no one has the incentive to address. Fake audience – bots clicking sites

Data sources are readily available lead by the Twitter or Facebook APIs. Get on github for open source code on downloading data

Was the 20th century an aberration as to how information was disseminated? We might just be going back to a world with pools of information.

What are the limits on what points of view any media company is willing to explore?